SVCFit: Inferring structural variant cellular fraction in tumors

Therapeutic pressure shapes tumor evolution by selecting for subclones with distinct genomic architectures that drive resistance. In metastatic castration-resistant prostate cancer (mCRPC), structural variants (SVs) are major drivers of this process but are rarely incorporated into clonal reconstruction from bulk DNA sequencing data. Here we describe SVCFit, a computational framework for estimating the cellular fraction of diverse classes of structural variants from whole-genome sequencing and using these estimates to reconstruct clonal evolutionary relationships. SVCFit explicitly models SV-type-specific breakpoint and breakend patterns and accounts for local copy-number context, enabling accurate estimation of SV cellular fraction across heterogeneous tumor genomes. Using simulated datasets and in silico mixtures of metastatic prostate cancer samples, we show that SVCFit achieves consistently lower estimation error than the current state-of-the-art approaches. We then apply SVCFit to longitudinal whole-genome sequencing data from patients with mCRPC treated with bipolar androgen therapy (supraphysiologic testosterone). Structural variant-based clonal phylogenies reveal marked treatment-associated clonal reconfiguration, including contraction of highly rearranged subclones and expansion of resistant populations defined by distinct structural alterations. Together, these results demonstrate that integrating structural variants into clonal evolutionary analysis provides critical insight into tumor evolution under therapy. SVCFit enables reconstruction of SV-defined clonal architecture from routine whole-genome sequencing, expanding the toolkit for studying cancer evolution in precision oncology.